TY - GEN
T1 - A Structural Information Guided Hierarchical Reconstruction for Graph Anomaly Detection
AU - Zou, Dongcheng
AU - Peng, Hao
AU - Liu, Chunyang
N1 - Publisher Copyright:
© 2024 ACM.
PY - 2024/10/21
Y1 - 2024/10/21
N2 - Anomalies in graphs involve attributes and structures and may occur at different levels (e.g., node or community). Existing GNN-based detection methods often merely focus on anomalies of single nodes or neighborhoods, making it hard to cope with complex and organized networks. Towards this, we propose SI-HGAD, a novel Graph Anomaly Detection (GAD) approach that utilizes hierarchical information to detect anomalies. Powered by structural information, SI-HGAD can mine an optimal graph abstraction while enabling hierarchical substructural modeling. Also, we design a Graph Transformer to mine multi-range structural and attribute patterns for nodes. The decoders reconstruct both the node attributes and the multi-level subgraphs in a bottom-up manner. Extensive experiments demonstrate the superiority of SI-HGAD.
AB - Anomalies in graphs involve attributes and structures and may occur at different levels (e.g., node or community). Existing GNN-based detection methods often merely focus on anomalies of single nodes or neighborhoods, making it hard to cope with complex and organized networks. Towards this, we propose SI-HGAD, a novel Graph Anomaly Detection (GAD) approach that utilizes hierarchical information to detect anomalies. Powered by structural information, SI-HGAD can mine an optimal graph abstraction while enabling hierarchical substructural modeling. Also, we design a Graph Transformer to mine multi-range structural and attribute patterns for nodes. The decoders reconstruct both the node attributes and the multi-level subgraphs in a bottom-up manner. Extensive experiments demonstrate the superiority of SI-HGAD.
KW - anomaly detection
KW - graph neural network
KW - structural information
UR - https://www.scopus.com/pages/publications/85210016771
U2 - 10.1145/3627673.3679869
DO - 10.1145/3627673.3679869
M3 - 会议稿件
AN - SCOPUS:85210016771
T3 - International Conference on Information and Knowledge Management, Proceedings
SP - 4318
EP - 4323
BT - CIKM 2024 - Proceedings of the 33rd ACM International Conference on Information and Knowledge Management
PB - Association for Computing Machinery
T2 - 33rd ACM International Conference on Information and Knowledge Management, CIKM 2024
Y2 - 21 October 2024 through 25 October 2024
ER -